Recently, learning algorithms motivated from sharpness of loss surface as an effective
measure of generalization gap have shown state-of-the-art performances. Nevertheless …

The recently proposed Sharpness-Aware Minimization (SAM) improves generalization by
minimizing a\textit {perturbed loss} defined as the maximum loss within a neighborhood in …

Abstract Models trained in federated settings often suffer from degraded performances and
fail at generalizing, especially when facing heterogeneous scenarios. In this work, we …

The Internet of Things (IoT) is on the verge of a major paradigm shift. In the IoT system of the
future, IoFT, the “cloud” will be substituted by the “crowd” where model training is brought to …

We systematize the approach to the investigation of deep neural network landscapes by
basing it on the geometry of the space of implemented functions rather than the space of …

Large learning rates, when applied to gradient descent for nonconvex optimization, yield
various implicit biases including the edge of stability (Cohen et al., 2021), balancing (Wang …

This work investigates formal generalization error bounds that apply to support vector
machines (SVMs) in realizable and agnostic learning problems. We focus on recently …

Recently, Sharpness-Aware Minimization (SAM) algorithm has shown state-of-the-art
generalization abilities in vision tasks. It demonstrates that flat minima tend to imply better …

Revolutionary advances in Internet of Things technologies have paved the way for a
significant increase in computational resources at edge devices that collect condition …

Several papers argue that wide minima generalize better than narrow minima. In this paper,
through detailed experiments that not only corroborate the generalization properties of wide …